HParams = {
    'num_mels': 80,  # Number of mel-spectrogram channels and local conditioning dimensionality
    #  network
    'rescale': True,  # Whether to rescale audio prior to preprocessing
    'rescaling_max': 0.9,  # Rescaling value
    # Use LWS (https://github.com/Jonathan-LeRoux/lws) for STFT and phase reconstruction
    # It's preferred to set True to use with https://github.com/r9y9/wavenet_vocoder
    # Does not work if n_ffit is not multiple of hop_size!!
    'use_lws': False,
    'n_fft': 800,  # Extra window size is filled with 0 paddings to match this parameter
    'hop_size': 200,  # For 16000Hz, 200 = 12.5 ms (0.0125 * sample_rate)
    # For 16000Hz, 800 = 50 ms (If None, win_size = n_fft) (0.05 * sample_rate)
    'win_size': 800,
    # 16000Hz (corresponding to librispeech) (sox --i <filename>)
    'sample_rate': 16000,
    # Can replace hop_size parameter. (Recommended: 12.5)
    'frame_shift_ms': None,
    # Mel and Linear spectrograms normalization/scaling and clipping
    'signal_normalization': True,
    # Whether to normalize mel spectrograms to some predefined range (following below parameters)
    # Only relevant if mel_normalization = True
    'allow_clipping_in_normalization': True,
    'symmetric_mels': True,
    # Whether to scale the data to be symmetric around 0. (Also multiplies the output range by 2,
    # faster and cleaner convergence)
    'max_abs_value': 4.,
    # max absolute value of data. If symmetric, data will be [-max, max] else [0, max] (Must not
    # be too big to avoid gradient explosion,
    # not too small for fast convergence)
    # Contribution by @begeekmyfriend
    # Spectrogram Pre-Emphasis (Lfilter: Reduce spectrogram noise and helps model certitude
    # levels. Also allows for better G&L phase reconstruction)
    'preemphasize': True,  # whether to apply filter
    'preemphasis': 0.97,  # filter coefficient.
    # Limits
    'min_level_db': -100,
    'ref_level_db': 20,
    'fmin': 55,
    # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To
    # test depending on dataset. Pitch info: male~[65, 260], female~[100, 525])
    'fmax': 7600,  # To be increased/reduced depending on data.
    ###################### Our training parameters #################################
    'img_size': 96,
    'fps': 25,
    'batch_size': 16,
    'initial_learning_rate': 1e-4,
    # ctrl + c, stop whenever eval loss is consistently greater than train loss for ~10 epochs
    'nepochs': 200000000000000000,
    'num_workers': 16,
    'checkpoint_interval': 3000,
    'eval_interval': 3000,
    'save_optimizer_state': True,
    # is initially zero, will be set automatically to 0.03 later. Leads to faster convergence.
    'syncnet_wt': 0.0,
    'syncnet_batch_size': 64,
    'syncnet_lr': 1e-4,
    'syncnet_eval_interval': 10000,
    'syncnet_checkpoint_interval': 10000,
    'disc_wt': 0.07,
    'disc_initial_learning_rate': 1e-4
}
